一种GPS/GLONASS/INS数据融合算法

针对卫星导航系统和惯性导航系统(INS)的不同特性,提出了一种GPS/GLONASS/INS数据融合算法。采用差分自适应检测算法、改进码平均相位算法以及位置联合解算方法实现了GPS/GLONASS数据融合,借助于改进的粒子滤波器、INS误差模型建立系统状态方程和观测方程,完成GPS/GLONASS系统速度值和INS系统速度值数据融合,提高组合导航系统精度和可靠性。使用真实数据对数据融合算法性能进行仿真分析,结果表明所设计算法是有效的,能够处理非线性非高斯条件下的滤波估计,提高滤波精度和系统可靠性。     

应用EKF平滑算法提高GPS/INS定位定姿精度

为了提高城市遮挡环境下GPS较长时间(60s)无法单独定位情况下GPS/INS组合定位定姿精度,研究了扩展卡尔曼滤波及其RTS(Rauch Tung Striebel)平滑算法;同时给出了基于ψ角惯导误差模型的GPS/INS组合系统状态方程和基于位置、速度更新的量测方程。实验中模拟GPS信号失锁60s,应用RTS后处理算法进行了GPS/INS组合数据处理。结果表明,扩展卡尔曼滤波EKF平滑算法可以有效地提高城市遮挡环境下GPS/INS组合定位定姿精度,特别是对GPS失锁的情况。从而很大程度上降低对高成本惯导的依赖。     

多模型卡尔曼滤波在地磁场航海测量中的应用

由于卡尔曼滤波对模型精度有很强的依赖性,因此实际的地磁场测量系统中,宜采用多模型自适应卡尔曼滤波,以提高状态估计的准确度。针对并行子滤波器间的数据融合问题,提出了"距离"意义下的数据融合新算法,应用结果表明算法具有实用性。探讨了通过检验并行子滤波器状态估计一致性的模型误差识别方法,并将其应用于监测地磁场测量系统中船舶磁化参数的变化。     

车辆导航系统的地图匹配技术研究

常规的地图匹配方法只能消除路段径向的偏差,在分析GPS误差模型的基础上,采用卡尔曼滤波技术对传感测量位置与数字道路地图之间的偏差进行估计,销减了路段平行方向的偏差,从而大大提高了系统的定位精度。仿真试验的结果证明了该算法对于提高车辆定位系统的性能是有效的。     

基于稀疏浮动车数据的城市路网交通流速度估计

浮动车数据在时空维度呈现较强的稀疏性,是其应用于城市路网交通流估计所面临的主要难题之一。本文通过分析路网交通流速度的时空特征,构建了一种基于朴素贝叶斯法的估计模型,实现对路网中未被样本覆盖路段交通流速度的估计。时间特征主要考虑目标路段相邻时段的交通流速度,空间特征根据路段间交通流相似关系进行分析,突破了传统基于欧氏空间或拓扑关系的度量方式。结果显示,模型能有效地估计出样本缺失路段的交通流速度,且在精度方面相对传统基于拓扑关系的算法优势显著,较好地解决了数据时空稀疏性问题,对基于浮动车数据的交通应用具有较强的实践意义。     

一种基于多普勒频移的GPS自适应滤波算法研究

针对接收机的动态模型对GPS定位精度的影响,提出了一种基于多普勒频移观测的高动态GPS自适应滤波算法。该算法利用GPS伪距测量值以及利用信号载波的多普勒频移所获得的伪距率测量值,在GPS动态滤波中同时观测伪距和伪距率。借助于移动目标的运动矢量模型以及GPS定位误差模型建立了滤波方程。重点讨论了运用该模型进行Kalman滤波的实现过程。仿真实验表明,该模型与传统的方差自适应模型相比,位置精度提高了32%、速度精度提高了25%,应用本文算法能够提高定位精度和改善接收机的动态性能,拓宽高精度、高动态导航的应用范围。     

高精度星载GPS实时定轨卡尔曼滤波模型

在动力学模型补偿算法的基础上,推导了星载GPS实时定轨的卡尔曼滤波模型。以此为理论基础,自主研制了星载GPS实时定轨软件SATODS。使用CHAMP卫星上的星载GPS实测伪距数据以及GPS卫星广播星历来模拟实时定轨数据处理,并将实时定轨结果与JPL精密轨道进行比较分析。结果表明,在滤波收敛后,实时定轨的轨道精度和速度精度的3dRMS分别可达到1.0m和1.2mm/s,受观测数据的GPS卫星数、PDOP值、粗差数据和数据中断等因素的影响较小。     

多维密度聚类的精细化道路交通运行状况检测

现有的路况检测方法以整条路段为单位进行检测,存在精度不高的问题,且DBSCAN算法用于出租车GPS数据聚类仍存在脱离线性参照系统、假噪声和簇内速度差异大等问题。在线性参照系统中定位GPS点,以两点间的测量值距离作为空间距离,同时增加速度距离约束,提出一种基于DBSCAN算法的多维密度聚类算法,使其适用于精细化路况检测;在此基础上构建路况事件表,并利用动态分段技术对路况事件进行管理和可视化,满足实际应用中对路况检测精度的要求。以上海市出租车GPS数据和路网数据为例进行实验分析,结果表明,提出的方法能够实现较为精细的路况检测。     

抗差估计在多站多星卡尔曼滤波精密定轨中的应用

针对在多站多星卡尔曼滤波定轨中,粗差探测判断标准选择不合适时会影响卡尔曼滤波解精度的问题,采用放宽粗差探测阈值并在滤波阶段采用抗差估计的方法来控制滤波的精度。利用IGS站和BD站的观测数据,分别计算了GPS、BD卫星单天弧度的定轨结果,并与IGS精密星历、武大精密星历作比较。结果表明,利用抗差卡尔曼滤波方法轨道精度得到了提高。     

一种提高导航精度的改进滤波方法

卡尔曼滤波技术是目前GPS/IMU组合导航中应用最广的误差估计关键技术之一。本文在捷联式惯导系统正向导航滤波算法与逆向导航滤波算法基础之上,提出一种将二者有机结合的组合滤波算法,用于事后IMU/GPS联合解算中,以提高组合导航的精度,并通过实际的机载飞行试验数据解算结果验证该方法的可行性。组合滤波后的位置精度达到厘米级,速度误差小于0.02 m/s,航向角精度约为0.2°。     

GPS手机的差分定位系统研究

提高GPS手机的定位精度具有很广阔的应用前景,本文研究GPS手机的实时定位差分系统的设计和实现方法。该系统采用基准站广播位置差分值、移动站实时处理的差分GPS方案,其主要优点是定位精度高、改正速度快、设备简单、操作方便;分析了位置差分GPS的定位误差,给出了测量结果。理论分析和测试结果都表明,该系统的差分定位精度比手机GPS直接定位相对提高31m左右,定位误差达4m左右,能够满足相关领域测量精度要求。     

GNSS增强系统中精密实时钟差高频估计及应用研究

GNSS星基差分增强系统依赖于实时轨道及钟差增强信息。本文主要研究多GNSS实时精密钟差估计模型,在传统非差基础上优化待估参数,实现了一种高效的Multi-GNSS实时钟差简化估计模型。基于PANDA软件开展了实时轨道数据处理与分析,经过验证可获得的GPS/北斗MEO/Galileo实时轨道径向精度1~5cm,北斗GEO/IGSO卫星径向精度约10cm。分析发现本文优化的实时钟差简化估计模型单历元解算效率较高,可应用于实时钟差增强信息高频(如1Hz)更新,且解算获得的实时钟差不存在常偏为绝对钟差;基于实时轨道,通过该模型可获得实时钟差精度GPS约0.22ns,北斗GEO约0.50ns、IGSO/MEO约0.24ns,Galileo约0.32ns。在此基础上,利用目前所获取的MultiGNSS实时数据流搭建了Multi-GNSS全球实时增强原型系统,并基于互联网实时播发增强信息,可初步实现实时PPP厘米级服务、伪距米级导航定位服务。     

Application of GPS in Traffic Management Systems

Integrated Traffic Management Systems (ITMS) need reliable, accurate, and real-time data. Travel time, speed, and delay are three of the most important factors used in ITMS for monitoring, quantifying, and controlling congestion. GPS has recently become available for civil applications. Because it provides real-time spatial and time measurements, it has an increasing use in conducting different transportation studies. This article presents the application of GPS in collecting travel time, speed, and delay information of 64 major roads in the state of Delaware. A comparative statistical analysis was performed on data collected by GPS, with data collected simultaneously by the conventional method. The GPS data proved to be at least as accurate as the data collected by the conventional method, and it was 50% more efficient in terms of manpower. Moreover, the sample-size requirement was determined to maintain 95% confidence level throughout the controlled test. Benefiting from the Geographic Information System's dynamic segmentation tool, our travel time, delay, and speed information were integrated with other relevant traffic data. This was presented graphically on the Internet for public use. Statistical trend analysis for the data collected in 1997, 1998, 1999, and 2000 are also presented and applications on the overall ITMS are discussed. ? 2002 Wiley Periodicals, Inc.     

Tests of phase center variations of various GPS antennas, and some results

Phase variations of GPS receiving antennas are a significant error component in precise GPS applications. A calibration procedure has been developed by Geo++ and the Institut für Erdmessung, which directly determines absolute phase center variations (PCVs) without any multipath influence by field measurements. The precision and resolution of the procedure allows the determination of reliable azimuthal variations. PCV may affect long-term static GPS differently than real-time GPS, depending on the applications. At the same time, different antenna types are involved. Less investigations have been done on absolute PCV of rover antennas than on geodetic antennas which, however, becomes more important due to the mixed antenna situation in GPS reference networks and RTK networks. The concepts of the absolute PCV field calibration are summarized and emphasis is placed on a variety of absolute PCV patterns of geodetic and rover antennas. Electronic Publication     

A real-time meteorological-based troposphere (RMT) correction with integrity bound for long baseline DGPS

This paper concentrates on the analysis of a real-time meteorological (MET)-based troposphere (RMT) model where MET data are used in real-time to provide troposphere error corrections with a bounded level of integrity for a prototype National Differential Global Positioning System-High Performance (NDGPS-HP) architecture. Toward this goal, three aspects are studied for this approach: sensitivity analysis, accuracy assessment, and integrity analysis. A Hopfield zenith delay and Chao mapping function models were chosen as a good compromise between accuracy and complexity in the integrity analysis. The sensitivity analysis results indicate that the Hopfield model is mostly sensitive to hot humid conditions, which is compounded slightly more and where some relative humidity sensors are less accurate. The accuracy assessment was performed with respect to both absolute and relative accuracy. In the absolute accuracy assessment, the comparison was made in terms of zenith troposphere delay estimation error, with respect to the International GPS Service (IGS) final troposphere zenith path delay (ZPD) product, which was used as the true ZPD. For locations where IGS stations are not available, a relative accuracy assessment was performed whereby comparisons were made in terms of GPS double difference (DD) carrier-phase troposphere correction residuals using various techniques. The accuracy assessment results indicate that the RMT has insignificant differences from the prototype National Oceanic and Atmosphere Administration (NOAA) troposphere error forecast model. An integrity analysis was performed, which presents integrity bounds for the RMT that can be applied to a NDGPS-HP architecture in which integrity requirements exist. The overriding goal of this effort was to establish a preliminary real-time troposphere error estimation model, with defined levels of integrity in its troposphere error estimation that can be included in an NDGPS-HP architecture, where integrity is a key system requirement. The conclusion is drawn that the RMT model may be well suited for a variety of users within a NDGPS-HP architecture. An erratum to this article can be found at     

组合GNSS系统定位数据质量与精度比较分析

本文对组合GNSS系统进行静态相对定位测量与RTK测量试验,研究结果表明:在静态相对定位测量中,BDS的数据利用率、多路径效应误差优于GPS与GLONASS,BDS的定位精度优于GLONASS略低于GPS;组合系统中GPS/BDS与GPS/BDS/GLONASS的定位精度较优,引入GLONASS对定位精度改善的作用不明显。在RTK测量中,当观测条件理想时,GPS/BDS较GPS可见卫星数目多,PDOP值低,中误差小。当观测条件较差时,GPS/BDS较GPS可见卫星数目多,PDOP平均值低,中误差小,限差内固定解获得时间减少76.1%;GPS/BDS/GLONASS较GPS/BDS在中误差、水平精度和垂直精度上更优,限差内固定解获得时间更稳定可靠。     

Comparing DGPS corrections prediction using neural network, fuzzy neural network, and Kalman filter

Position information obtained from standard global positioning system (GPS) receivers has time variant errors. For effective use of GPS information in a navigation system, it is essential to model these errors. A new approach is presented for improving positioning accuracy using neural network (NN), fuzzy neural network (FNN), and Kalman filter (KF). These methods predict the position components’ errors that are used as differential GPS (DGPS) corrections in real-time positioning. Method validity is verified with experimental data from an actual data collection, before and after selective availability (SA) error. The result is a highly effective estimation technique for accurate positioning, so that positioning accuracy is drastically improved to less than 0.40 m, independent of SA error. The experimental test results with real data emphasize that the total performance of NN is better than FNN and KF considering the trade-off between accuracy and speed for DGPS corrections prediction.     

基于自然样条补偿最小二乘的高程误差研究

基于非参数自然样条补偿最小二乘估计方法,把GPS高程数据与对应的水准数据的差值作为观测值,按照非参数自然样条补偿最小二乘的估计方法,得到消弱偶然误差v之后的平差值,并以此去改正GPS高程,可以取得较好的效果。并以某河道测量采集的数据作为事例,证明该方法的有效性。     

A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications

The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively.     

GPS-based precise orbit determination of Low Earth Orbiters with limited resources

The combination of GPS measurements and high-fidelity dynamic models via a Kalman filter/smoother, known as the reduced dynamic technique, allows 3D positioning of Low Earth Orbiters to the sub-decimeter level. Such accuracies can only be achieved if the GPS data are nearly continuous, post-processed and a dual-frequency receiver is utilized. The focus of this study is to quantitatively analyze the degradations in position accuracy in the presence of various limitations or constraints, which can be brought on by mission hardware limitations, for example, on micro- or nanosatellites. The constraints explored in this study are as follows: the use of single-frequency data only; real-time processing; limited dynamic modeling due to computing capabilities; and non-continuous GPS receiver operation due to power limits. The experiments are conducted with 6-h data arcs for 7 separate days using data from the CHAllenging Mini-Satellite Payload. A 3D root mean square (rms) error of 15 cm is observed in the best-case solution, in which dual-frequency data are post-processed with all available data. Various levels of accuracy degradations are observed as constraints are placed on this best-case solution. The 3D rms error of the post-processed, single-frequency solution is 68 cm and 1.3 m for the real-time, dual-frequency solution. In very challenging environments, for example, with the receiver on for only 10 min of a 90-min orbit, the 3D rms increases to 350 m.